1 Technical University of Cluj-Napoca Abstract -The paper proposes a new multiobjective optimization method, based on fuzzy techniques. The method performs a real multiobjective optimization, every parameter modification taking into account the unfulfillment degrees of all the requirements. It uses fuzzy sets to define fuzzy objectives and fuzzy systems to compute new parameter values. The stra...

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one...

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes an multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving on...

Smoothed analysis of multiobjective 0–1 linear optimization has drawn considerable attention recently. In this literature, the number of Pareto-optimal solutions (i.e., solutions with the property that no other solution is at least as good in all the coordinates and better in at least one) for multiobjective optimization problems is the central object of study. In this paper, we prove several l...

We propose a novel multiobjective evolutionary algorithm, MEDACO, a shorter acronym for MOEA/D-ACO, combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The motivation is to use the online-learning capabilities of ACO, according to the Reactive Search Optimization (RSO) paradigm of ”learning while optimizing”, to further improve the ...

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-h...

In the first part of this study we have introduced six different multiobjective dual problems to a general multiobjective optimization problem, for which we presented weak as well as strong duality assertions. Afterwards, we derived some inclusion results for the image sets of three of these problems. The aim of this second part is to complete our investigations by studying the relations betwee...

As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m - 1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobject...

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands a user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers m...

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary during the last two decades. However, as another famous framework, decomposition-based not received sufficient attention from optimization. In this paper, we make use of solve problems (COPs). our method, first all, a COP is transformed into biobjective problem (BOP). Afterward, BOP deco...